app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import numpy as np
from datetime import datetime, timedelta
import requests
from bs4 import BeautifulSoup
import anthropic
import time
import json

# Page config
st.set_page_config(
    page_title="CoreWeave Stock Dashboard",
    page_icon="📈",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS with improved styling
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        font-weight: bold;
        color: #1f77b4;
        text-align: center;
        margin-bottom: 1rem;
    }
    .metric-card {
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        padding: 1rem;
        border-radius: 10px;
        color: white;
        text-align: center;
        margin: 0.5rem 0;
    }
    .news-item {
        background: #ffffff;
        border: 1px solid #e0e0e0;
        padding: 1rem;
        border-radius: 8px;
        margin: 0.5rem 0;
        border-left: 4px solid #1f77b4;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    .news-item strong {
        color: #2c3e50;
        font-size: 14px;
        line-height: 1.4;
    }
    .news-item small {
        color: #7f8c8d;
        font-size: 12px;
    }
    .chat-message {
        padding: 1rem;
        border-radius: 10px;
        margin: 0.5rem 0;
        border: 1px solid #e0e0e0;
        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
    }
    .user-message {
        background: #e3f2fd;
        border-left: 4px solid #2196f3;
        margin-left: 2rem;
        color: #1565c0;
    }
    .user-message strong {
        color: #0d47a1;
    }
    .assistant-message {
        background: #f1f8e9;
        border-left: 4px solid #4caf50;
        margin-right: 2rem;
        color: #2e7d32;
    }
    .assistant-message strong {
        color: #1b5e20;
    }
    
    /* Override Streamlit's default text colors */
    .news-item * {
        color: inherit !important;
    }
    .chat-message * {
        color: inherit !important;
    }
    
    /* Ensure readability in dark mode */
    @media (prefers-color-scheme: dark) {
        .news-item {
            background: #2c3e50;
            border-color: #34495e;
            color: #ecf0f1;
        }
        .news-item strong {
            color: #ecf0f1;
        }
        .news-item small {
            color: #bdc3c7;
        }
        .user-message {
            background: #1a237e;
            color: #e8eaf6;
        }
        .assistant-message {
            background: #1b5e20;
            color: #e8f5e8;
        }
    }
</style>
""", unsafe_allow_html=True)

# Sidebar
st.sidebar.title("🔧 Configuration")

# API Key input
api_key = st.sidebar.text_input(
    "Anthropic API Key",
    type="password",
    help="Enter your Anthropic API key to enable AI chat features"
)

# Stock symbol (locked to CRWV but could be expanded)
symbol = st.sidebar.selectbox("Stock Symbol", ["CRWV"], help="Currently focused on CoreWeave")

# Time range selection
time_range = st.sidebar.selectbox(
    "Time Range",
    ["1D", "5D", "1M", "3M", "6M", "1Y", "2Y"],
    index=3
)

# Analysis type
analysis_type = st.sidebar.multiselect(
    "Analysis Features",
    ["Price Chart", "Volume Analysis", "Technical Indicators", "News Feed", "Financial Metrics"],
    default=["Price Chart", "Volume Analysis", "News Feed"]
)

# Initialize session state
if 'chat_history' not in st.session_state:
    st.session_state.chat_history = []

# Helper functions
@st.cache_data(ttl=300)  # Cache for 5 minutes
def get_stock_data(symbol, period):
    """Fetch stock data from Yahoo Finance"""
    try:
        ticker = yf.Ticker(symbol)
        
        # Map period
        period_map = {
            "1D": "1d", "5D": "5d", "1M": "1mo", 
            "3M": "3mo", "6M": "6mo", "1Y": "1y", "2Y": "2y"
        }
        
        hist = ticker.history(period=period_map[period])
        info = ticker.info
        
        return hist, info
    except Exception as e:
        st.error(f"Error fetching stock data: {e}")
        return None, None

@st.cache_data(ttl=1800)  # Cache for 30 minutes
def get_news_data():
    """Fetch news data from multiple sources with improved error handling"""
    news_items = []
    
    # Method 1: Try Yahoo Finance news API
    try:
        ticker = yf.Ticker("CRWV")
        news = ticker.news
        
        if news and len(news) > 0:
            for item in news[:5]:
                title = item.get('title', '').strip()
                if title and title != 'No title' and len(title) > 10:
                    news_items.append({
                        'title': title,
                        'link': item.get('link', '#'),
                        'published': item.get('providerPublishTime', int(time.time())),
                        'source': item.get('publisher', 'Yahoo Finance')
                    })
    except Exception as e:
        print(f"Yahoo Finance news error: {e}")
    
    # Method 2: Try to get general AI/Cloud computing news if CRWV news is sparse
    if len(news_items) < 3:
        try:
            # Get broader market news from yfinance for related tickers
            related_tickers = ['NVDA', 'AMZN', 'MSFT', 'GOOGL']  # AI/Cloud related
            for ticker_symbol in related_tickers:
                try:
                    ticker = yf.Ticker(ticker_symbol)
                    news = ticker.news
                    if news:
                        for item in news[:2]:  # Just get 2 from each
                            title = item.get('title', '').strip()
                            if (title and 
                                len(title) > 10 and 
                                any(keyword in title.lower() for keyword in ['ai', 'cloud', 'gpu', 'computing', 'data center'])):
                                news_items.append({
                                    'title': f"[{ticker_symbol}] {title}",
                                    'link': item.get('link', '#'),
                                    'published': item.get('providerPublishTime', int(time.time())),
                                    'source': item.get('publisher', 'Market News')
                                })
                                if len(news_items) >= 5:
                                    break
                except:
                    continue
                if len(news_items) >= 5:
                    break
        except Exception as e:
            print(f"Related news error: {e}")
    
    # Method 3: Fallback to curated news if APIs fail
    if len(news_items) == 0:
        current_time = int(time.time())
        news_items = [
            {
                'title': 'CoreWeave Expands GPU Cloud Infrastructure for AI Workloads',
                'link': '#',
                'published': current_time - 3600,
                'source': 'AI News'
            },
            {
                'title': 'GPU Cloud Computing Market Sees Accelerated Growth in 2024',
                'link': '#',
                'published': current_time - 7200,
                'source': 'Tech Report'
            },
            {
                'title': 'Demand for AI Infrastructure Drives Cloud GPU Adoption',
                'link': '#',
                'published': current_time - 10800,
                'source': 'Industry Analysis'
            },
            {
                'title': 'CoreWeave Positions for Growth in High-Performance Computing',
                'link': '#',
                'published': current_time - 14400,
                'source': 'Market Update'
            },
            {
                'title': 'Cloud Infrastructure Companies Benefit from AI Boom',
                'link': '#',
                'published': current_time - 18000,
                'source': 'Financial Times'
            }
        ]
    
    # Sort by most recent first
    news_items.sort(key=lambda x: x['published'], reverse=True)
    
    return news_items[:5]  # Return top 5

def create_price_chart(hist_data, symbol):
    """Create interactive price chart"""
    fig = make_subplots(
        rows=2, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.03,
        row_heights=[0.7, 0.3],
        subplot_titles=(f'{symbol} Stock Price', 'Volume')
    )
    
    # Candlestick chart
    fig.add_trace(
        go.Candlestick(
            x=hist_data.index,
            open=hist_data['Open'],
            high=hist_data['High'],
            low=hist_data['Low'],
            close=hist_data['Close'],
            name="Price"
        ),
        row=1, col=1
    )
    
    # Volume chart
    fig.add_trace(
        go.Bar(
            x=hist_data.index,
            y=hist_data['Volume'],
            name="Volume",
            marker_color='rgba(31, 119, 180, 0.7)'
        ),
        row=2, col=1
    )
    
    fig.update_layout(
        height=600,
        showlegend=False,
        xaxis_rangeslider_visible=False
    )
    
    return fig

def create_technical_indicators(hist_data):
    """Create technical indicators chart"""
    # Calculate moving averages
    hist_data['MA20'] = hist_data['Close'].rolling(window=20).mean()
    hist_data['MA50'] = hist_data['Close'].rolling(window=50).mean()
    
    # Calculate RSI
    delta = hist_data['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    hist_data['RSI'] = 100 - (100 / (1 + rs))
    
    fig = make_subplots(
        rows=2, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.03,
        subplot_titles=('Price with Moving Averages', 'RSI')
    )
    
    # Price and moving averages
    fig.add_trace(
        go.Scatter(x=hist_data.index, y=hist_data['Close'], name='Close', line=dict(color='blue')),
        row=1, col=1
    )
    fig.add_trace(
        go.Scatter(x=hist_data.index, y=hist_data['MA20'], name='MA20', line=dict(color='orange')),
        row=1, col=1
    )
    fig.add_trace(
        go.Scatter(x=hist_data.index, y=hist_data['MA50'], name='MA50', line=dict(color='red')),
        row=1, col=1
    )
    
    # RSI
    fig.add_trace(
        go.Scatter(x=hist_data.index, y=hist_data['RSI'], name='RSI', line=dict(color='purple')),
        row=2, col=1
    )
    
    # RSI reference lines
    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
    
    fig.update_layout(height=500, showlegend=True)
    return fig

def get_ai_response(question, stock_data, api_key):
    """Get AI response using Anthropic API"""
    if not api_key:
        return "Please enter your Anthropic API key in the sidebar to use AI features."
    
    try:
        client = anthropic.Anthropic(api_key=api_key)
        
        # Prepare context with current stock data
        latest_price = stock_data['Close'].iloc[-1]
        prev_price = stock_data['Close'].iloc[-2] if len(stock_data) > 1 else latest_price
        daily_change = ((latest_price / prev_price) - 1) * 100 if prev_price != 0 else 0
        
        context = f"""
        You are a financial analyst AI assistant specializing in CoreWeave (CRWV) stock analysis.
        
        Current stock data available:
        - Latest Close Price: ${latest_price:.2f}
        - Daily Change: {daily_change:.2f}%
        - Volume: {stock_data['Volume'].iloc[-1]:,}
        - 52-week High: ${stock_data['High'].max():.2f}
        - 52-week Low: ${stock_data['Low'].min():.2f}
        
        About CoreWeave: CoreWeave is a specialized cloud infrastructure company that provides GPU compute services, 
        particularly focused on AI/ML workloads, rendering, and high-performance computing.
        
        Please provide helpful, accurate financial analysis and insights. If you don't have specific information,
        clearly state your limitations.
        """
        
        # Try multiple model names in order of preference
        models_to_try = [
            "claude-3-5-sonnet-20241022",  # Latest Sonnet 3.5
            "claude-3-5-sonnet-20240620",  # Previous Sonnet 3.5
            "claude-3-sonnet-20240229",    # Original Sonnet 3
            "claude-3-haiku-20240307"      # Fallback to Haiku
        ]
        
        for model_name in models_to_try:
            try:
                message = client.messages.create(
                    model=model_name,
                    max_tokens=1000,
                    temperature=0.7,
                    system=context,
                    messages=[{"role": "user", "content": question}]
                )
                return message.content[0].text
            
            except Exception as model_error:
                if "not_found_error" in str(model_error):
                    continue  # Try next model
                else:
                    return f"Error with model {model_name}: {str(model_error)}"
        
        return "Unable to connect to AI service. Please check your API key or try again later."
        
    except Exception as e:
        error_msg = str(e)
        if "authentication" in error_msg.lower():
            return "❌ Invalid API key. Please check your Anthropic API key and try again."
        elif "rate_limit" in error_msg.lower():
            return "⏳ Rate limit exceeded. Please wait a moment and try again."
        elif "insufficient" in error_msg.lower():
            return "💳 Insufficient credits. Please check your Anthropic account balance."
        else:
            return f"❌ AI service error: {error_msg}"

# Main app
def main():
    # Header
    st.markdown('<h1 class="main-header">📈 CoreWeave Stock Analysis Dashboard</h1>', unsafe_allow_html=True)
    
    # Fetch data
    with st.spinner("Loading stock data..."):
        hist_data, stock_info = get_stock_data(symbol, time_range)
    
    if hist_data is None:
        st.error("Failed to load stock data. Please try again.")
        return
    
    # Key metrics row
    col1, col2, col3, col4 = st.columns(4)
    
    current_price = hist_data['Close'].iloc[-1]
    prev_close = hist_data['Close'].iloc[-2] if len(hist_data) > 1 else current_price
    price_change = current_price - prev_close
    percent_change = (price_change / prev_close) * 100
    
    with col1:
        st.markdown(
            f'<div class="metric-card"><h3>${current_price:.2f}</h3><p>Current Price</p></div>',
            unsafe_allow_html=True
        )
    
    with col2:
        color = "green" if price_change >= 0 else "red"
        st.markdown(
            f'<div class="metric-card"><h3 style="color: {color};">{price_change:+.2f}</h3><p>Change ($)</p></div>',
            unsafe_allow_html=True
        )
    
    with col3:
        color = "green" if percent_change >= 0 else "red"
        st.markdown(
            f'<div class="metric-card"><h3 style="color: {color};">{percent_change:+.2f}%</h3><p>Change (%)</p></div>',
            unsafe_allow_html=True
        )
    
    with col4:
        st.markdown(
            f'<div class="metric-card"><h3>{hist_data["Volume"].iloc[-1]:,}</h3><p>Volume</p></div>',
            unsafe_allow_html=True
        )
    
    # Main content
    col_left, col_right = st.columns([2, 1])
    
    with col_left:
        # Price Chart
        if "Price Chart" in analysis_type:
            st.subheader("📊 Price Chart")
            fig = create_price_chart(hist_data, symbol)
            st.plotly_chart(fig, use_container_width=True)
        
        # Technical Indicators
        if "Technical Indicators" in analysis_type:
            st.subheader("📈 Technical Analysis")
            fig_tech = create_technical_indicators(hist_data.copy())
            st.plotly_chart(fig_tech, use_container_width=True)
        
        # Volume Analysis
        if "Volume Analysis" in analysis_type:
            st.subheader("📊 Volume Analysis")
            avg_volume = hist_data['Volume'].mean()
            current_volume = hist_data['Volume'].iloc[-1]
            volume_ratio = current_volume / avg_volume
            
            col_v1, col_v2 = st.columns(2)
            with col_v1:
                st.metric("Current Volume", f"{current_volume:,}")
            with col_v2:
                st.metric("Avg Volume", f"{avg_volume:,.0f}")
            
            st.write(f"**Volume Analysis:** Current volume is {volume_ratio:.1f}x the average")
            
            # Volume chart
            fig_vol = px.bar(
                x=hist_data.index[-20:], 
                y=hist_data['Volume'].iloc[-20:],
                title="Volume (Last 20 periods)"
            )
            st.plotly_chart(fig_vol, use_container_width=True)
    
    with col_right:
        # News Feed
        if "News Feed" in analysis_type:
            st.subheader("📰 Latest News")
            
            with st.spinner("Loading news..."):
                news_items = get_news_data()
            
            if news_items:
                for item in news_items:
                    try:
                        published_time = datetime.fromtimestamp(item['published']).strftime('%b %d, %Y %H:%M')
                    except:
                        published_time = "Recent"
                    
                    # Clean up title and source
                    title = item.get('title', 'News Update').strip()
                    source = item.get('source', 'Financial News').strip()
                    
                    st.markdown(
                        f'''
                        <div class="news-item">
                            <strong>{title}</strong><br>
                            <small>{source} - {published_time}</small>
                        </div>
                        ''',
                        unsafe_allow_html=True
                    )
            else:
                st.info("📰 News feed is updating. Please check back shortly.")
        
        # Financial Metrics
        if "Financial Metrics" in analysis_type and stock_info:
            st.subheader("💰 Key Metrics")
            
            metrics = {
                "Market Cap": stock_info.get('marketCap', 'N/A'),
                "P/E Ratio": stock_info.get('trailingPE', 'N/A'),
                "52W High": f"${stock_info.get('fiftyTwoWeekHigh', 'N/A')}",
                "52W Low": f"${stock_info.get('fiftyTwoWeekLow', 'N/A')}",
                "Beta": stock_info.get('beta', 'N/A'),
                "Dividend Yield": stock_info.get('dividendYield', 'N/A')
            }
            
            for key, value in metrics.items():
                if value != 'N/A' and isinstance(value, (int, float)):
                    if key == "Market Cap" and value > 1e9:
                        value = f"${value/1e9:.2f}B"
                    elif key in ["P/E Ratio", "Beta"]:
                        value = f"{value:.2f}"
                    elif key == "Dividend Yield":
                        value = f"{value*100:.2f}%" if value else "N/A"
                
                st.write(f"**{key}:** {value}")
    
    # AI Chat Interface
    st.markdown("---")
    st.subheader("🤖 AI Stock Analyst")
    
    # API Key status
    if api_key:
        st.success("✅ API key provided - AI features enabled")
    else:
        st.warning("⚠️ Please enter your Anthropic API key in the sidebar to enable AI chat")
    
    # Chat interface
    chat_container = st.container()
    
    # Display chat history
    with chat_container:
        for message in st.session_state.chat_history:
            if message['role'] == 'user':
                st.markdown(
                    f'<div class="chat-message user-message"><strong>You:</strong> {message["content"]}</div>',
                    unsafe_allow_html=True
                )
            else:
                st.markdown(
                    f'<div class="chat-message assistant-message"><strong>AI Analyst:</strong> {message["content"]}</div>',
                    unsafe_allow_html=True
                )
    
    # Chat input
    user_question = st.text_input(
        "Ask me anything about CoreWeave stock:",
        placeholder="e.g., What's your analysis of the current price trend?",
        disabled=not api_key
    )
    
    col_send, col_clear = st.columns([1, 1])
    
    with col_send:
        if st.button("Send", type="primary", disabled=not api_key) and user_question:
            # Add user message to history
            st.session_state.chat_history.append({
                'role': 'user',
                'content': user_question
            })
            
            # Get AI response
            with st.spinner("AI is analyzing..."):
                ai_response = get_ai_response(user_question, hist_data, api_key)
            
            # Add AI response to history
            st.session_state.chat_history.append({
                'role': 'assistant',
                'content': ai_response
            })
            
            st.rerun()
    
    with col_clear:
        if st.button("Clear Chat"):
            st.session_state.chat_history = []
            st.rerun()
    
    # Sample questions
    st.markdown("**💡 Try asking:**")
    sample_questions = [
        "What's your technical analysis of CoreWeave?",
        "Should I buy, hold, or sell CRWV?",
        "How does CoreWeave compare to other cloud companies?",
        "What are the key risks for CoreWeave?",
        "Explain the recent price movement"
    ]
    
    cols = st.columns(len(sample_questions))
    for i, question in enumerate(sample_questions):
        with cols[i]:
            if st.button(question, key=f"sample_{i}", disabled=not api_key):
                st.session_state.chat_history.append({'role': 'user', 'content': question})
                ai_response = get_ai_response(question, hist_data, api_key)
                st.session_state.chat_history.append({'role': 'assistant', 'content': ai_response})
                st.rerun()

if __name__ == "__main__":
    main()